Revisiting the Hype Curve
Hype is defined as promoting or publicizing something, often exaggerating its importance or benefits. In our business, hype has been associated with EHRs and some of its associated elements such as Computer Decision Support, Artificial Intelligence, Interoperability and blockchain. A more benign cousin of hype is enthusiasm: intense and eager enjoyment, interest, or approval. Enthusiasm might moderate the exaggeration associated with hype, but might none-the-less be misplaced with respect to the true value of the thing about which one is enthusiastic. Both hype and professed enthusiasm can be influenced by proprietary interests.
There is often a time element associated with hype and enthusiasm in that the degree of each may vary with time, sometimes appropriately and sometimes inappropriately. Enthusiasm can be influenced by experience to date. When there is little early experience it is relatively easy to be highly enthusiastic because there are no results to dampen one’s zeal. (This is sure to be great!) With more time enthusiasm might be tempered by actual experience. Good experiences might support early expectations and adoptions, and perhaps drive even greater enthusiasm. Bad experience should logically reduce enthusiasm, although sometimes enthusiasm is resilient enough to overcome reality, or bad experience might be correctly or incorrectly viewed as something to be overcome with further effort. (Yes, current EHRs have lots of issues but this doesn’t mean EHRs aren’t great.)
Enthusiasm should properly be based on specific and measurable expectations. If someone is enthusiastic about something they should be able to articulate with specificity why this is the case and be willing to subject their enthusiasm to actual measurement. Or if not measurement there should at least be a clear line of reasoning between that which one is enthusiastic about and how that thing will result in the claimed benefits. This is a version of my multi-part test of proposed wonderous new ideas. What exactly is the problem you are trying to solve? How exactly will what you are proposing solve that problem? How will we know if it did? There are also operational questions associated with any proposed solution such as: How long will it take? How much will it cost to develop and use? Who has the expertise to do it? Are there alternatives, and if so why are we doing this instead of one of them? It might also be remembered that new ideas have to implemented, raising further training and time. A broader question is whether addressing a particular problem is the best way to use our limited resources? This can be challenging to those with a passionate cause, and invites the bogus answer of “If we can save just one life”.
We might also ask what can go wrong, and what are the consequences of it going wrong? In device design this takes the form of Failure Modes and Effects Analysis (FMEA). Here enthusiasm might be challenged by some healthy (but realistic) pessimism, but this invites the misplaced challenge of “Why are you always so negative?”
Some ideas or systems or products may have an appropriate level of enthusiasm over some period of time. Others may never fulfill their early expectations and some with potential value might be killed off by unfulfilled promises. In the medical device sector there are devices that have come and gone as it was discovered that the ideas behind them, and/or their execution, actually wasn’t very good. This is sometimes associated with inadequate pre-market testing, or where a clinical trial does not capture longer term effects over broader patient populations, and when used by those with more diverse skill levels. But as asserted in the Netflix medical device documentary The Bleeding Edge, it sometimes takes more evidence to get rid of something than it did to bring it into existence. There are similar effects with EHRs and its associated products as “this will be great” meets reality.
Note: There are many explanations and critiques of the hype curve available.